Assessment of right ventricular (RV) function and volume is important in the diagnosis of cardiovascular diseases. Clinical measurements, such as the RV ejection fraction (EF) and volumes, have diagnostic, prognostic, and therapeutic uses in association with various pathologies, such as for determining cardiac function in patients with acquired heart disease.
Magnetic resonance (MR) imaging allows an exhaustive RV evaluation with high spatial resolution, and provides a large number of images. MR imaging has several advantages over other imaging techniques (e.g., echocardiography), including excellent image quality and lack of geometric assumptions. For quantitative functional analysis and to obtain clinical measurements such as EF, it is essential to delineate the RV. Manual delineation of the RV boundary in all MR images is tedious and time-consuming, and recent research has been dedicated to automating the delineation process.
Due to its complex morphology and function, assessment of the RV is acknowledged as a more challenging problem than the assessment of the left ventricle. The problem becomes more difficult due to thin and ill-defined RV borders, its crescent shaped structure, and the complex deformations of the RV chamber. Further, RV segmentation methods should consider the photometric similarities between the connected cardiac regions. For example, the papillary muscles and heart wall have approximately the same luminous intensity. Therefore, standard segmentation methods based solely on intensity information cannot yield accurate tracking.
To overcome these difficulties, existing methods use atlas-based techniques or prior geometric properties, such as the shape of the RV learned a priori from a finite-training set. If only shapes similar to the training set are allowed, the use of active shape and appearance models can lead to a realistic solution. However, the optimization of such models does not always guarantee the global optima. The main drawbacks of statistical shape or atlas based approaches are the need for large manually segmented training sets and the results being highly dependent on the choice of the training data. The results are often biased towards a particular cardiac pathology.
Further, the shape of the RV is significantly different at end-systole in comparison to end-diastole. Therefore, in general, it is more difficult to obtain a good segmentation of the RV at end-systole than at end-diastole using the shape-based approaches. Due to its smaller size, inaccuracies in the segmentation of the RV at end-systole affect the clinical measurements such as EF significantly.